IEEE Signal Processing - March 2018 - 174

include ultrasound imaging, ultrasound
elastography and thermography, and
ultrasound system design. He is a staff
systems engineer at Siemens Healthineers in Seattle, Washington.

References

[1] V. Rieke and K. Butts Pauly, "MR thermometry,"
J. Magn. Reson. Imaging, vol. 27, no. 2, pp. 376-
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[2] C. Simon, P. VanBaren, and E. S. Ebbini, "Twodimensional temperature estimation using diagnostic ultrasound," IEEE Trans. Ultrason. Ferroelect.
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[3] M. Bayat, J. R. Ballard, and E. S. Ebbini, "In
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Feb. 2015.

TiPS & TRickS

[9] E. S. Ebbini, "Noninvasive two-dimensional
temperature imaging for guidance of thermal
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[5] R. M. Arthur, D. Basu, Y. Guo, J. W. Trobaugh,
and E. G. Moros, "3-D in vitro estimation of temperature using the change in backscattered ultrasonic
energy," IEEE Trans. Ultrason. Ferroelect. Freq.
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[10] D. C. Youla, "Mathematical theory of image
restoration," in Image Recovery: Theory and Application, H. Stark, Ed. London: Academic, 1987,
pp. 29-77.

[6] M. Pernot, M. Tanter, J. Bercoff, K. R. Waters,
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Biomed. Eng., vol. 57, no. 1, pp. 12-16, Jan. 2010.
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[11] A. N. Amini, E. S. Ebbini, and T. T. Georgiou,
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SP

(continued from page 164)

can be used in applications that process
real-valued signals in an analysis-modification-synthesis framework, where
processing only the RDFT can provide
a reduction in computational complexity for the case of a densely sampled
DFT. In contrast to previous methods
of signal reconstruction from partial
DFT information (i.e., DFT magnitude
or phase), the reconstructed signal from
the procedure described in this tutorial
has no errors when compared with the
original signal.

Authors
Stephen So (s.so@griffith.edu.au)
received his B.Eng. degree (first class
honors) in microelectronic engineering and his Ph.D. degree in image and
speech signal processing from Griffith
University, Brisbane, Australia, in 1999
and 2005, respectively. Currently he is a
senior lecturer in the Griffith School of
Engineering, Gold Coast campus, Griffith
University. He is a Senior Member of the
IEEE and was a member of the editorial
board of Digital Signal Processing
174

[4] N. R. Miller, J. C. Bamber, and P. M. Meaney,
"Fundamental limitations of noninvasive temperature imaging by means of ultrasound echo strain
estimation," Ultrasound Med. Biol., vol. 28, no. 10,
pp. 1319-1333, 2002.

(Elsevier) from 2013 to 2015. His
research interests are in image and speech
coding, speech enhancement and noise
reduction, distributed speech recognition,
electrocardiography signal processing,
machine learning, and deep neural networks.
Kuldip K. Paliwal (k.paliwal@griffith
.edu.au) received his B.S. degree from Agra
University, India, in 1969, his M.S. degree
from Aligarh Muslim University, India, in
1971, and his Ph.D degree from Bombay
University, India, in 1978. Since July 1993,
he has been a professor with Griffith University, Brisbane, Australia, in the Griffith
School of Engineering. His current
research interests include speech recognition, speech coding, speaker recognition, speech enhancement, face
recognition, image coding, pattern recognition, and artificial neural networks. He
has published more than 300 papers in
these research areas. He is on the editorial board of IEEE Signal Processing
Magazine and also served as an associate
editor of IEEE Signal Processing Letters
from 1997 to 2000. He has coedited two
IEEE Signal Processing Magazine

|

March 2018

|

books Speech Coding and Synthesis
(Elsevier) and Speech and Speaker Recognition: Advanced Topics (Kluwer).

References

[1] P. L. Van Hove, M. H. Hayes, J. S. Lim, and A.
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[4] A. V. Oppenheim and R. W. Schafer, DiscreteTime Signal Processing, 3rd ed. Englewood Cliffs,
NJ: Prentice Hall, 2010.
[5] Y. Zhang and Y. Zhao, "Real and imaginary
modulation spectral subtraction for speech enhancement," Speech Commun. vol. 55, no. 4, pp. 509-522,
May 2013.
[6] B. Schwerin and K. K. Paliwal, "Using STFT
real and imaginary parts of modulation signals for
MMSE-based speech enhancement," Speech Commun., vol. 58, pp. 49-68, Mar. 2014.
[7] D. W. Griffin and J. S. Lim, "Signal estimation
from modified short-time Fourier transform," IEEE
Trans. Acoust. Speech Signal Process., vol. ASSP32, no. 2, pp. 236-243, Apr. 1984.
[8] J. W. Hung, J. H. Hsieh, and B. Chen, "Robust
speech recognition via enhancing the complex-valued acoustic spectrum in modulation domain,"
IEEE Trans. Audio, Speech, Language Process.
vol. 24, no. 2, pp. 236-251, Feb. 2016.

SP



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